A hybrid particle swarm optimization for component placement in 3D IC design

This paper deals with a component placement algorithm for 3D IC design. The Particle Swarm Optimization (PSO) is a general purpose stochastic algorithm mimicking the behaviors of particles self-organizing a system. The size of solution space is very large in the 3D component placement problem and it is afraid that the objective function value will be degraded. The Clustering Algorithm (CA) is an efficient initial placement algorithm and this algorithm is used for partitioning the placement problem into clusters with the total pseudo wire-length minimization. PSO is applied to each of the clusters for determining the detailed placement of components with the acceleration as well as the objective function optimization. This hybrid PSO (CA-PSO) is experimentally evaluated against a component placement problem of actual printed wiring board consisting of 217 components and 462 nets and the results show its feasibility.